Representation Techniques Research Articles

Privacy-preserving for assembly deviation prediction in a machine learning model of hydraulic equipment under value chain collaboration

Hydraulic equipment, as a typical mechanical product, has been wildly used in various fields. Accurate acquisition and secure transmission of assembly deviation data are the most critical issues for hydraulic equipment manufacturer in the PLM-oriented value chain collaboration. Existing deviation prediction methods are mainly used for assembly quality control, which concentrate in the product design and assembly stage. However, the actual assembly deviations generated in the service stage can be used to guide the equipment maintenance and tolerance design. In this paper, a high-fidelity prediction and privacy-preserving method is proposed based on the observable assembly deviations. A hierarchical graph attention network (HGAT) is established to predict the assembly feature deviations. The hierarchical generalized representation and differential privacy reconstruction techniques are also introduced to generate the graph attention network model for assembly deviation privacy-preserving. A derivation gradient matrix is established to calculate the defined modified necessary index of assembly parts. Two privacy-preserving strategies are designed to protect the assembly privacy of node representation and adjacent relationship. The effectiveness and superiority of the proposed method are demonstrated by a case study with a four-column hydraulic press.

Multi-relational graph attention networks for knowledge graph completion

Knowledge graphs are multi-relational data that contain massive entities and relations. As an effective graph representation technique based on deep learning, graph neural network has reported outstanding performance for modeling knowledge graphs in recent studies. However, previous graph neural network-based models have not fully considered the heterogeneity of knowledge graphs. Furthermore, the attention mechanism has demonstrated its great potential in many areas. In this paper, a novel heterogeneous graph neural network framework based on a hierarchical attention mechanism is proposed, including entity-level, relation-level, and self-level attentions. Thus, the proposed model can selectively aggregate informative features and weights them adequately. Then the learned embeddings of entities and relations can be utilized for the downstream tasks. Extensive experimental results on various heterogeneous graph tasks demonstrate the superior performance of the proposed model compared to several state-of-the-art methods.

Emotion Recognition for Partial Faces Using a Feature Vector Technique.

Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively.

The influence of virtual human representations on first-year architecture students’ perceptions of digitally designed spaces: a pilot study

ABSTRACT The use of human figures throughout the design process enables designers to experience, communicate, and evaluate design concepts. Earlier studies of architectural representation sought to identify the relationship between the tools and techniques of representation and how individuals perceive architectural scenes. Nonetheless, the effect of virtual human representation (VHR) on students’ perceptions of architectural space remains underexplored. Therefore, an experimental study was conducted with first-year architecture students, in which they experienced three architectural scenes (non-presence of VHR, presence of idle VHR, and presence of animated VHR). Statistical analyses of the students’ self-assessments examined how the inclusion of VHRs in digitally visualized architectural spaces influenced the perceptions of these spaces. The study revealed that incorporating VHR into representations of architectural spaces positively affected the students’ sense of physical dimensions and of the spatial qualities.

BINARY SIGNED-DIGIT REPRESENTATIONS IN PAPERFOLDING

AbstractWhen a page, represented by the interval $[0,1]$ , is folded right over left $n $ times, the right-hand fold contains a sequence of points. We specify these points using two different representation techniques, both involving binary signed-digit representations.

Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network

In recent years, slow feature analysis (SFA) has been successfully employed to deal with the air handling unit (AHU) system’s time-varying dynamic properties. However, since the derived slow features are the linear combinations of all original variables, the conventional SFA based methods may suffer from poor model interpretability and result in inaccurate fault detection and diagnosis (FDD) performance. To enhance the dynamic AHU system FDD effectiveness, this paper presents a novel feature sparse representation based SFA algorithm through imposing the sparsity on the slow features, which is called the sparse three-way data based dynamic SFA (STBDSFA). We make two contributions to propose the STBDSFA approach. One contribution is to infuse the feature sparse representation technique into the constructed SFA based monitoring model by performing the sparse restriction on the loading vector, which can effectively eliminate the meaningless variables’ coupling and select the key variable responsible for the fault detection. Before building the STBDSFA algorithm, the other contribution is to construct a new three-way data based dynamic SFA (TBDSFA) monitoring model to handle the AHU system’s two-directional dynamic properties. In the established TBDSFA model, the auto-regressive moving average exogenous (ARMAE) model is first adopted to reveal the variables’ auto-correlation relationships. Then, the multiway data analysis is applied to figure out the batch-wise dynamic property in multiple batch runs. After that, the dynamic SFA model is further set up to sufficiently tackle the time-wise dynamic nature within a batch run. To effectively diagnose the pattern of detected fault, another innovative work is to develop a long short-term memory (LSTM) based fault identification approach to classify the sparse slow features of fault snapshot dataset, owning to the LSTM’s superiority of coping with the dynamic time-varying nature of the sparse slow features. By comparing with some traditional and closely related FDD methods, the case study on the ASHRAE Research Project RP-1312 experimental datasets are performed to verify the performance and effectiveness of the proposed FDD scheme for the AHU system. To be specific, in comparison with the sparse principal component analysis, sparse dynamic SFA, kernel locality preserving projection and sparse three-way data based SFA approaches, the developed STBDSFA based monitoring method not only reveals no fault detection delays to alert the eleven test faults, but also achieves the highest average fault detection rates, i.e., 99.31% for the T2 statistic and 99.77% for the SPE statistic. Contrasted with the support vector machine, temporal convolutional network, convolutional neural network and deep belief network based classification algorithms, the suggested STLSTM based fault identification approach obtains the highest average fault diagnosis rate, i.e., 95.51% for the eleven fault snapshot datasets.

New avenues and challenges in semantic map research (with a case study in the semantic field of emotions)

AbstractIn this paper, we present an overview of the methods associated with semantic maps, focusing on current challenges and new avenues for research in this area, which are at the core of the contributions to this special issue. Among the fundamental questions are: (1) the validity of the basic assumption, namely, to what extent does coexpression reflect semantic similarity; (2) the central problem of identifying analytical primitives in the domain of semantics; (3) the methods of inference used for creating coexpression maps and the representation techniques (graph structure vs. Euclidean space) as well as their respective merits (including the goodness of fit of the models); and (4) the use of semantic maps to support diachronic and synchronic descriptions of individual languages. In order to illustrate and discuss key aspects, we conduct an experiment in the semantic field of emotions, for which we construct a classical semantic map based on the dataset of CLICS3.

Machine learning assisted screening framework for insecticidal peptides

Pest management in agriculture relies on the use of chemical pesticides. These chemicals are harmful to both the environment and to human health. This has necessitated the search for sustainable alternatives. In this regard, natural products and “biopesticides” are promising for sustainable crop protection. A handful of proteins and peptides with insecticidal properties have been extracted from different organisms till date. However, experimental screening and identification of insecticidal peptides is laborious and resource-intensive. In this study, we have developed a computational screening framework that can aid in the identification of insecticidal peptides. We have used non-linear support vector machine (SVM) algorithm to develop a binary classifier that can detect whether a peptide sequence is likely to have insecticidal property. To develop the binary classifier model, we have implemented three feature representation techniques namely, amino acid compositions (AAC, 20-dimensions), composition transition and distribution (CTD, 147-dimensions), and conjoint triad feature (CTF, 343-dimensions). Results obtain indicate that the CTF method outperforms the other two methods with an accuracy of about 0.947. Thus, the proposed approach can be used as a tool for sustainable agriculture.

Ethnographic narratives as living archives among the Iraqi diaspora

Iraqis in Iraq and the diaspora, through their life experience and interpretation of events, can be a site of a living archive. They possess knowledge about how national and imperial events impacted people on the ground, and about daily life and struggle to reckon with the past and the present, imagine the future and carve a sense of belonging through storytelling. The ability to provide a personal account of the events they lived through becomes a venue to challenge their portrayal as sectarian subjects in mainstream media. In this article, I argue that life stories can serve as anticolonial methodology, techniques of representation and empowerment, and sites of knowledge production about daily lives and struggles. As a living archive, life stories act as a platform to bear witness, challenge essentialist misconceptions, expose colonial and imperial realities and constitute individuals as subjects empowered to provide an account of their own lives.

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm.

Purpose: The technique of fusing or integrating medical images collected from single and various modalities is known as medical image fusion. This is done to improve the quality of the images and combine information from several medical images. The whole procedure aids medical practitioners in gaining correct information from single images. Image fusion is one of the fastest-growing research topics in the medical imaging field. Sparse modeling is a popular signal representation technique used for image fusion with different dictionary learning approaches. We propose a medical image fusion with sparse representation (SR) and block total least-square (BLOTLESS) update dictionary learning. Approach: The domain of dictionary learning is the most significant research domain related to SR. An efficient dictionary increases the effectiveness of sparse modeling. Due to SR being an ongoing interesting research area, the medical image fusion process is done with a modified image fusion framework with recently developed BLOTLESS update dictionary learning. Results: The experimental results are compared for the image fusion process using other state-of-the-art dictionary learning algorithms, such as simultaneous codeword optimization, method of optimal directions, and K-singular value decomposition. The effectiveness of the algorithm is evaluated based on image fusion quantitative parameters. Results show that the BLOTLESS update dictionary algorithm is a promising modification for the sparse-based image fusion with its applicability in the fusion of images related to different diseases. Conclusions: The experiments and results show that the dictionary learning algorithm plays an important role in the sparse-based image fusion general framework. The fusion results also show that the proposed improved image fusion framework for medical images is promising compared with frameworks with other dictionary learning algorithms. As an application, it is also used as a tool for the fusion of different modularities of images related to brain tumor and glioma.

Enabling spatial thinking through an augmented reality for teaching crystal structure

ABSTRACT The crystal structure is an important topic in the STEM field and the basic knowledge to determine the material structure and its behaviour. There is a wealth of literature on three-dimensional (3D) and augmented reality (AR) technology to represent 3D crystal environments to provide a new way of visualisation and to foster spatial skills. This article aimed to develop and evaluate an AR system for teaching Bravais Crystals in Natural Science, especially the potential of AR as an innovative teaching tool to encourage spatial skills. The users were satisfied with the AR system (Mean = 4.3, SD = 0.49) to learn spatial representation techniques in a cooperative learning environment. The AR system provided excitement and motivation and was attractive and flexible while allowing the students to engage in independent learning.

Urban-GAN: An artificial intelligence-aided computation system for plural urban design

The current urban design computation is mostly centered on the professional designer while ignoring the plural dimension of urban design. In addition, available public participation computational tools focus mainly on information and idea sharing, leaving the public excluded in design generation because of their lack of design expertise. To address such an issue, this study develops Urban-GAN, a plural urban design computation system, to provide new technical support for design empowerment, allowing the public to generate their own designs. The sub-symbolic representation and artificial intelligence techniques of deep convolutional neural networks, case-based reasoning, and generative adversarial networks are used to acquire and embody design knowledge as the density function, and generate design schemes with this knowledge. The system consists of an urban form database and five process models through which the user with little design expertise can select urban form cases, generate designs similar to those cases, and make design decisions. The Urban-GAN is applied to hypothetical design experiments, which show that the user is able to apply the system to successfully generate distinctive designs following the urban form “styles” in Manhattan, Portland, and Shanghai. This study further extends the discussion about the plural urban design computation to general reflections on the goals and values in AI technique application in planning and design.

Summarization of financial reports with TIBER

This paper reports an approach for summarizing financial texts that combine several techniques for sentence representation and neural document modeling. Our approach is extractive and it follows the classic pipeline of ranking and consequent selecting of the top-ranked text chunks. We evaluate our method on the financial reports provided in the Financial Narrative Summarization (FNS 2021) shared task. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange. The reports composed FNS 2021 dataset are very long, have many sections, and are written in “financial” language using various special terms, numerical data, and tables. The results show that our approach outperforms the FNS topline with a very serious advantage. In addition to its performance, our approach is also time-efficient.

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

AbstractThe rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

Data Representation by Joint Hypergraph Embedding and Sparse Coding

Matrix factorization (MF), a popular unsupervised learning technique for data representation, has been widely applied in data mining and machine learning. According to different application scenarios, one can impose different constraints on the factorization to find the desired basis, which captures high-level semantics for the given data, and learns the compact representation corresponding to the basis. We note that almost all previous work on MF in data mining has ignored to find such a basis, which can carry high-order semantics in the data. In this article, we propose a novel MF framework called Joint Hypergraph Embedding and Sparse Coding (JHESC), in which the obtained basis captures high-order semantic information in data. Specifically, we first propose a new hypergraph learning model to obtain a more discriminative basis by hypergraph-based Laplacian Eigenmap, then sparse coding is conducted on the learned basis such that the new representation has stronger identification capability. In addition, we extend the proposed method to the reproducing kernel Hilbert space for dealing with nonlinear data more effectively. Extensive experimental results on data clustering demonstrate that the proposed method consistently outperforms the other state-of-the-art matrix factorization methods.

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.

LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications.

Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques

A novel multimodal framework for automatic recognition of individual cattle based on hybrid features using sparse stacked denoising autoencoder and group sparse representation techniques

An Ontology-based Knowledge Mining Model for Effective Exploitation of Agro Information

The quality of agriculture depends on the quality of the yield, which is usually obtained through the well-being of the crop. The quality of any crop depends on the minerals in the soil, the type of soil, the location, and the seasons. The crop yield depends on soil fertility, availability of water, climate, and disease prevention. Although this information is prevailing in plenty among the expert farmers, the means of abducting the information to the future generation has not been much promoted. Hence, the knowledge disseminated regarding agriculture becomes scarce, affecting the entire agricultural process. Given these facts, a single source, strong knowledge management system is proposed to be designed. The system aims to embrace the different kinds of knowledge associated with agriculture and attempt to obtain a single source of agro information that is very much usable and reusable to the users. To ensure the maximum level of reusability, the knowledge of the domain needs to be modeled and represented in a way that is scalable and flexible. One of the knowledge representation techniques that emphasizes on reusability and scalability is ontology. Thus, this paper attempts to design an ontology-based agro knowledge management system. A rule base is constructed to improve the expressiveness of the knowledge. An incremental mining approach is adopted to extract the knowledge from multiple ontology. To understand better to aid decision-making, a visualization task is carried out. A multi ontology-based knowledge mining model is attempted in this research to provide better insight regarding agro knowledge.

Comic Empires: Imperialism in Cartoons, Caricature, and Satirical Art

Comic Empires: Imperialism in Cartoons, Caricature, and Satirical Art